Apparatus and method for fluid property measurements

ABSTRACT

In some embodiments, apparatus and systems, as well as methods, may operate to measure formation fluid and obtain data, the data having measurement levels that vary over a parameter. The data is grouped in one or more categories, each category having data falling within a range, and the grouped data is analyzed as a function of the parameter. In some embodiments, the grouped data is used to identify at least one fluid type of the formation fluid using the grouped data.

RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.12/673,686, filed Nov. 11, 2010 which is a nationalization under 35U.S.C. 371 of PCT/US2008/006045, filed May 9, 2008, and published as WO2009/025688 A1 on Feb. 26, 2009, which claims priority to U.S.Provisional Patent Application Ser. No. 60/965,351, filed Aug. 20, 2007;which applications and publication are incorporated herein by referencein their entirety and made a part hereof.

TECHNICAL FIELD

Various embodiments described herein relate to determiningcharacteristics of geological formations, including density andporosity.

BACKGROUND

In the process of exploration and development of hydrocarbons, wells aredrilled using drilling fluids. These drilling fluids are composed ofliquids that are weighted with fine grained solids like barite whichincreases the density of the drilling fluids to exceed the pressure ofthe fluids in the formation rock pores. This keeps the formation fluidin place while drilling and prevents the formations fluids from beingproduced to the surface in an uncontrollable manner which is commonlyknow as a blowout. Since the pressure of the mud system exceeds theformation pore pressure the mud fluids (know as filtrate) will flow intothe formation. This process is called invasion. The mud systems aredesigned to minimize this invasion by forming a mud cake composed of thesolids being deposited on the well bore walls. It is desirable to obtainformation samples to prove the existence of producible hydrocarbons inthe rock pores. In a down hole fluid sampling process, the primaryobjective is to obtain or identify formation samples representative oftrue, for example, clean formation fluid or native fluid with a lowcontamination level of borehole fluids or drilling fluids.

During the pumping process, physical and chemical properties offormation fluids being extracted from the formation can be measuredusing sensors placed along the flowline of the tool. The sensormeasurements are used to try and identify the fluid type, and tocalculate the contamination level.

The pumped fluids usually consist of a mixture that is segregated wherea single measurement in time cannot be made that accurately representsthe bulk fluid mixture, and the data measurements are erratic. While anaverage of the data can be made, the average of the data often fails toaccurately identify the fluid type and levels of contamination.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a side, cut-away view of a down hole tool accordingto various embodiments.

FIG. 2 illustrates a block diagram of a system according to variousembodiments.

FIG. 3 illustrates a cross-sectional view of a portion of the down holetool according various embodiments.

FIG. 4 illustrates a portion of the down hole tool according variousembodiments.

FIG. 5 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 6 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 7 illustrates a method flow diagram according to variousembodiments.

FIG. 8 illustrates a method flow diagram according to variousembodiments.

FIG. 9 illustrates a method flow diagram according to variousembodiments.

FIG. 10 illustrates a method flow diagram according to variousembodiments.

FIG. 11 illustrates a method flow diagram according to variousembodiments.

FIG. 12 illustrates a block diagram of an article according to variousembodiments.

FIG. 13 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 14 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 15A illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 15B illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 16 illustrates a graph illustrating raw data measurements.

FIG. 17 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 18 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIGS. 19A-19B illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 20 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 21 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 22 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 23 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 24 illustrates a block diagram of a system according to variousembodiments.

FIG. 25 illustrates a block diagram of a system according to variousembodiments.

FIG. 26 illustrates a block diagram of a system according to variousembodiments.

FIG. 27 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 28 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 29 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

FIG. 30 illustrates a method flow diagram according to variousembodiments.

FIG. 31 illustrates a method flow diagram according to variousembodiments.

FIG. 32 illustrates a graph illustrating data measurements over aparameter according to various embodiments.

DETAILED DESCRIPTION

In the following description of some embodiments of the presentinvention, reference is made to the accompanying drawings which form apart hereof, and in which are shown, by way of illustration, specificembodiments of the present invention which may be practiced. In thedrawings, like numerals describe substantially similar componentsthroughout the several views. These embodiments are described insufficient detail to enable those skilled in the art to practice thepresent invention. Other embodiments may be utilized and structural,logical, and electrical changes may be made without departing from thescope of the present invention. The following detailed description isnot to be taken in a limiting sense, and the scope of the presentinvention is defined only by the appended claims, along with the fullscope of equivalents to which such claims are entitled.

A method and apparatus is provided herein that allows for fluid types tobe identified with greater accuracy, for example during the samplingprocess. During drilling or formation testing operations, the formationfluid is tested to determine the presence of certain fluids. Duringcollection of fluid samples, the fluid can be homogenous and/orheterogeneous, and granular flow properties may also exist.

The embodiments herein utilize the effects of segregation to improve theaccuracy of fluid property measurements. The embodiments includeidentifying fluid changes that vary over a parameter such as, but notlimited to, time, fluid sensor measurement, piston position, or volume,and analyze when the fluid changes occur, based on the parameter, anddata can be obtained as to the fluid type, and/or contamination of thefluid as a function of the parameter. Using this analysis, the systemcan predict the occurrence of a fluid type such as gas, oil, water, orcontaminate. A sample chamber valve can be selectively opened and closedto accept the desired fluid into a sample chamber.

FIGS. 1-4 illustrate systems and portions of the system for implementingthe embodiments. FIG. 1 illustrates an option for a system 100 fordrilling operations, in accordance with embodiments of the invention. Itshould be noted that the system 100 can also include a system forpumping operations, or other operations. The system 100 includes a downhole tool 124 that is associated with a drilling rig 102 located at asurface 104 of a well. The drilling rig 102 provides support for thedown hole tool, including a drill string 108. The drill string 108penetrates a rotary table 110 for drilling a borehole 112 throughsubsurface formations 114. The drill string 108 includes a Kelly 116 (inthe upper portion), a drill pipe 118 and a bottom hole assembly 120(located at the lower portion of the drill pipe 118). The bottom holeassembly 120 may include drill collars 122, a down hole tool 124 and adrill bit 126. The down hole tool 124 may be any of a number ofdifferent types of tools including measurement-while-drilling (MWD)tools, logging-while-drilling (LWD) tools, etc.

During drilling operations, the drill string 108 (including the Kelly116, the drill pipe 118 and the bottom hole assembly 120) may be rotatedby the rotary table 110. In addition or alternative to such rotation,the bottom hole assembly 120 may also be rotated by a motor that is downhole. The drill collars 122 may be used to add weight to the drill bit126. The drill collars 122 also optionally stiffen the bottom holeassembly 120 allowing the bottom hole assembly 120 to transfer theweight to the drill bit 126. The weight provided by the drill collars122 also assists the drill bit 126 in the penetration of the surface 104and the subsurface formations 114.

During drilling operations, a mud pump 132 optionally pumps drillingfluid, for example, drilling mud, from a mud pit 134 through a hose 136into the drill pipe 118 down to the drill bit 126. The drilling fluidcan flow out from the drill bit 126 and return back to the surfacethrough an annular area 140 between the drill pipe 118 and the sides ofthe borehole 112. The drilling fluid may then be returned to the mud pit134, for example via pipe 137, and the fluid is filtered. The drillingfluid cools the drill bit 126 as well as provides for lubrication of thedrill bit 126 during the drilling operation. Additionally, the drillingfluid removes the cuttings of the subsurface formations 114 created bythe drill bit 126.

The down hole tool 124 may include one to a number of different sensors145, which monitor different down hole parameters and generate data thatis stored within one or more different storage mediums, for example,within the down hole tool 124. The type of down hole tool 124 and/or thetype of sensors 145 thereon may be dependent on the type of down holeparameters being measured. Such parameters may include density,resistivity, capacitance, dielectric properties, acoustic properties,Nuclear Magnetic Resonance (NMR) properties, bubble point, temperature,optical, chemical, component or combinations thereof. The sensors areconfigured to periodically measure a down hole fluid property of aparameter, such as, but not limited to, time, volume, pump pistonposition, or valve status (open or closed). A processor is furtheroptionally included and is operable to group fluid measurements in oneor more categories, each category having fluid measurements fallingwithin a range. The processor is operable to group the fluidmeasurements as a function of the parameter, and to identify a fluidtype based on the grouping of fluid measurements.

The down hole tool 124 further includes a power source 149, such as abattery or generator. A generator could be powered either hydraulicallyor by the rotary power of the drill string. The down hole tool 124includes a formation testing tool 150, which can be powered by powersource 149. In an embodiment, the formation testing tool 150 is mountedon a drill collar 122. The formation testing tool 150 engages the wallof the borehole 112 and extracts a sample of the fluid in the adjacentformation via a flow line.

FIG. 1 further illustrates an embodiment of a wireline system 170 thatincludes a down hole tool 171 coupled to a base 176 by a logging cable174. The logging cable 174 may include, but is not limited to, awireline (multiple power and communication lines), a mono-cable (asingle conductor), and a slick-line (no conductors for power orcommunications). The base 176 is positioned above ground and optionallyincludes support devices, communication devices, and computing devices.The tool 171 houses a formation testing tool 150 that acquires samplesfrom the formation. In an embodiment, the power source 149 is positionedin the tool 171 to provide power to the formation testing tool 150. Thetool 171 may further include additional testing equipment 172, such asthe sensors and/or processor as discussed above. The sensors can beconfigured to periodically measure a down hole fluid property over aparameter, as discussed further below. In operation, a wireline system170 is typically sent down hole after the completion of a portion of thedrilling. More specifically, the drill string 108 creates a borehole112. The drill string is optionally removed and the wireline system 170is inserted into the borehole 112 for testing the formation fluid.Alternatively a logging while drilling system can be designed withsimilar capabilities to a wireline tool and does not require the drillstring to be removed before sampling.

FIG. 2 illustrates a block diagram including a pump 202, and a sensor,such as a density sensor, at the outlet of the pump 202. In an option,one or more sample chambers 204 are included so that selective samplesof the fluid can be taken. Sample chamber valves 206 can be selectivelyopened during the cycle of the pump to retrieve a fluid sample during aselected phase of the pump stroke. For instance, once the methodology isused to correlate when a certain fluid type can be found at a certainvalue of a parameter, such as at a certain time, volume, or pump strokeposition, the sample chamber valves 206 can be opened at the value wherethe desired fluid type can be sampled. In a further option, multiplevalves 206 and multiple sample chambers 204 can be incorporated so thatdifferent samples can be taken at different fluid phases. For instance,a first sample chamber can sample water, and a second sample chamber cansample oil. In another option, another sample chamber can sample gas. Inyet another option, the sample chambers can be used to extractcontaminated fluid. In yet another option, the valves 206 can be used toexclude a particular fluid type.

FIG. 3 illustrates an example of a device that can be used to displacefluid either from the well bore from the chamber 216, such as a pump202, which can assist in segregating the fluid. Other types of devicesor methods can be used to segregate the formation fluid. For instance,the formation fluid can segregate naturally or induced by gravitational,or centrifugal forces or electrical potential. Other fluid propertiessuch as viscosity, capillary pressure, immiscibility and electricalconduction effects can also cause fluids to separate.

The pump 202 includes a piston 210 which moves from a position a 0%,212, to a position at 100%, 214. As the piston 210 moves within thechamber 216 of the pump 202, the piston 210 displaces fluid from thechamber 216 through the flow line 218, where the flow line 218 has asmaller diameter than the chamber 216.

A sensor 145 can be disposed along the flow line 218, allowing for oneor more properties of the formation fluid to be measured as the piston210 displaces fluid from the chamber 216. For example, the sensor canmeasure density, resistivity, capacitance, dielectric properties,acoustic properties, NMR properties, bubble point, temperature, opticalproperties, or combinations thereof. In an option, as the sensormeasures the fluid property, a parameter such as piston position can bedetected and noted so that fluid properties are taken at differentvalues of the parameter (i.e. the piston position). The piston positionis merely one option of a parameter, however, other parameters such as,but not limited to, volume, time, fluid sensor measurement can be used.

In an example, the sensor can measure a density of the fluid at variouspositions of the piston 212. The changes in the density measurements canbe separated and grouped in to measurement bins. The measure bins, in anoption, are defined by a range of density indicating a fluid type. Thebins allow for analysis and categorization of the fluid types as afunction of a parameter, such as piston stroke position.

The fluid type changes over the changing parameter (i.e. piston strokeposition), as shown for example in FIGS. 4 and 5. In FIG. 4 it can beseen that the fluid discharged begins as a first fluid type 218 having adensity falling within a first range, changing to a second fluid type220 having a density falling within a second range, and further changingto a third fluid type 222 having a density falling within a third range,depending on the stroke position of the piston. FIG. 5 illustrates thetransition of the fluid from water to gas as the piston stroke positionmoves from 0% to 100%.

The sensor measurements can be plotted and correlated with one or moreparameters, such as piston stroke position, as shown in FIG. 5, oranother parameter, such as time. FIG. 6 illustrates the measurementsmade of the formation fluid as a function of time. FIG. 6 furtherillustrates the cyclical nature of the density measurements, andresulting fluid identification over a parameter, such as time. Thismethod may use the cyclical nature of the measurement to determine afluid cycle over time, and may also calculate or correct the position ofthe piston as it relates to actual movement of fluids, variation of theslip velocity, or compressibility of the fluid(s).

A method for determining fluid types with a system is further described.Referring to FIG. 7, the method includes measuring, for instance,formation fluid 302 or a fluid property of formation fluid in a boreholewith a down hole tool and obtaining data 304, where the data hasmeasurement levels that vary over a parameter. Examples of parametersinclude, but are not limited to, time, volume expelled from a chamber,piston stroke position, sensor measurements, or combinations thereof.Measuring the formation fluid optionally includes measuring for one ormore of density, resistivity, capacitance, dielectric properties,acoustic properties, NMR properties, bubble point, temperature, optical,chemical, or compositional properties.

The method further includes grouping data in one or more categories 306,where each category has data falling within a range. Optionally, themethod further includes manipulating the fluid, such as pumping thefluid, or segregating the fluid, for instance while measuring the fluid.

Further included in the method is analyzing the grouped data as afunction of the parameter 308. Analyzing the grouped data optionallyincludes identifying at least one fluid type of the formation fluidusing the grouped data, and/or characterizing a heterogeneity of theformation fluid. In another option, analysis the grouped data optionallyincludes analyzing the grouped data as a function of time, amplitude,i.e. amplitude of the piston stroke position, or volume, or combinationsthereof. Further options for the method include selectively sampling thefluid, or selectively excluded. For instance, the fluid is sampled orexcluded after the grouped data is identified as a particular fluidtype. In another option, the method can be used to estimatecontamination of the sample.

An example of density sensor measurements is shown in FIG. 28. Assumingthe in-situ oil density is less than water, the water filtrate isdetected with the high density measurements ρ_(max) and the oil (or gas)with the lower measurements, ρ_(min). The base-line averaged curve is anestimate of the fluid mixture ρ_(mix). By making the assumption thatthese early measurements actually detect the water filtrate and thenative formation fluids, it is possible to make an estimate of the fluidcontamination C as follows:

$C = {\frac{\rho_{mix} - \rho_{\min}}{\rho_{\max} - \rho_{\min}}(\%)}$

This contamination estimate also assumes the mixing model is linear withdensity. Now assuming the standard deviations of the measurements areknown, an additive error analysis is used to estimate the standard errorof contamination.

$S_{c} = {C\sqrt{\frac{\sigma_{mix}^{2} + \sigma_{\min}^{2}}{\left( {\rho_{mix} - \rho_{\min}} \right)^{2}} + \frac{\sigma_{\max}^{2} + \sigma_{\min}^{2}}{\left( {\rho_{\max} - \rho_{\min}} \right)^{2}}}}$

Using the density sensor resolution of 0.003 gm/cc as the standard errorfor the measurements, a standard error of 1.4% is determined from theabove equation for a typical WBM and oil sample (i.e., ρ_(max)=1.0,ρ_(min)=0.7) at low levels of contamination. A similar contaminationanalysis can be made using the resistivity or types of fluid sensorresponses.

Further details for the method as are follows. In a system as describedabove, a series of measurement values S can be taken over a parameter,such as time, and each reading in the series has a unique index, i. Theseries of measurement values can be grouped or binned into unique curvesusing several techniques. In an option, a technique involves groupingthe bins into different reading levels as shown in FIG. 8. In this logicdiagram, three levels are shown (i.e., A, B, C) but the method is notnecessarily limited to three, and can include additional levels. In anoption, A represents the cutoff measurement or a range for a first fluidtype, such as gas, so that a sensor measurement less than A would begiven a unique bin identifier of j=1 to be plotted as a unique Curve 1representing the sensor gas readings. If the sensor reading is in therange between A and B, it could be classified as a second fluid type,such as an oil, and given a unique bin identifier of j=2 and plotted asCurve 2. If the last bin represented the cutoff or range for a thirdfluid type, such as water, then any reading greater than C would havej=3 and plotted as Curve 3. Additional ranges are possible to identifyadditional fluid types. Each curve can have a unique visual identifierfor each fluid type. For example, each curve can have a different colorto show the fluid type (i.e., red, green, blue for gas, oil and waterrespectively).

In another option for the method, a method for sampling formation fluidincludes introducing formation fluid into a pump, the pump having aninlet and an outlet. The method further includes segregating the fluidwithin the pump, pumping fluid out through a flowline, and sensing fluidwith a fluid sensor and obtaining sensor data, where sensing fluidincludes sensing fluid with the fluid sensor at or downstream of theoutlet of the pump. The method further includes selectively opening andclosing a valve and accepting a desired fluid in at least one samplechamber based on the sensor data, where the valve is open and closedwhile the fluid is pumped out through the flowline.

Various options for the method are as follows. For instance, the methodis optionally performed until at least one sample chamber is filled. Inanother option, the method includes filling one sample chamber with onefluid type and additional sample chambers are filled with at least oneother fluid type. In another option, sensing fluid with the fluid sensorincludes sensing density of the fluid, resistivity, capacitance,dielectric properties, acoustic properties, NMR properties, bubblepoint, temperature, optical, chemical, compositional or combinationsthereof. The method can also be used in conjunction with the grouping orbinning techniques described herein.

Referring to FIG. 9, the data can be further processed or analyzed todetermine the fractional quantities of the bins. For example, consider aseries of measurements where m is the number of measurements made. Thenumber of measurements could be based on a parameter such as a timeinterval, volume expelled, or some logical event in the testing toollike the stroke of a pump, or a fraction of the pump stroke. Using mmeasurements, the fractional flow of fluid types can be determined asfollows.

${{Type}\mspace{14mu} 1},{{{Gas}\mspace{14mu}{Fraction}} = \frac{\sum\limits_{i}^{i + m}{{for}\mspace{14mu}\left( {i,j} \right)\mspace{14mu}{if}\mspace{14mu}\left( {{j = 1},1,0} \right)}}{m}}$${{Type}\mspace{14mu} 2},{{{Oil}\mspace{14mu}{Fraction}} = \frac{\sum\limits_{i}^{i + m}{{for}\mspace{14mu}\left( {i,j} \right)\mspace{14mu}{if}\mspace{14mu}\left( {{j = 2},1,0} \right)}}{m}}$${{Type}\mspace{14mu} 3},{{{Water}\mspace{14mu}{Fraction}} = \frac{\sum\limits_{i}^{i + m}{{for}\mspace{14mu}\left( {i,j} \right)\mspace{14mu}{if}\mspace{14mu}\left( {{j = 3},1,0} \right)}}{m}}$${{Type}\mspace{14mu} n},{{Fraction} = \frac{\sum\limits_{i}^{i + m}{{for}\mspace{14mu}\left( {i,j} \right)\mspace{14mu}{if}\mspace{14mu}\left( {{j = n},1,0} \right)}}{m}}$

These quantities can be plotted on a curve from 0 to 1 showing the fluidfraction changes over a parameter, such as time. This is an alternativeway of plotting the data that can supplement plotting the binned curves1, 2, 3 . . . n. In another option, the data can be grouped using therelative velocities of the fluid fractions. In this case, the changes inthe fluid fractions are tracked within the m measurements. By knowingthe total fluid velocity, the relative velocities of each fluid fractioncan be determined and plotted.

Using the type of grouping or binning shown in FIG. 8 applied over areoccurring event such as a pump stroke, a new type of processing can beapplied to produce weighted average curves. This is illustrated in FIG.9, where k represents a reoccurring event such as the pump pistonposition over the pump cycle. For example, k could be a fraction from 0to 1 where 0 represents the pump piston in its uppermost position and 1in its lowest position. During a pump cycle, there is a maximum andminimum value recorded during this period (i.e., S_(max), S_(min)). Fromthese maxima and minima, a sensor cutoff, S_(co), can be establishedsuch as some fraction between the S_(max) and S_(min) values. Twocategories or bins can be established similar to the logic of FIG. 8,where the values above and below the sensor cutoff, S_(co), are stored.The stored values can be averaged creating two curves where one is theweighted average above the S_(co) and the other below.

In another option, the method of binning or grouping the data by aparameter including tool events, such as fractions of the pumpdisplacement. In this case, where k represents fractions of a pumpstroke, data that is recorded during fractional displacement of the pumpcan be recorded in bins and plotted as separate curves (i.e., k=¼, ½, ¾,1). The fractional periods do not necessarily need to be evenly spaced.Also, other parameters including tool events can be used, such as theduration of a valve opening or closing.

FIG. 10 illustrates a method for grouping data such as the sensormeasurements into categories or bins that are related to a parametersuch as the pump piston position. Because the pump or other reoccurringevents in the tool can tend to separate fluids, the binned curvesdefined by this logic provide a method of observing fluid mixturechanges over time and help to determine when to take a representativesample of fluid.

The data obtained via the sensor measurements can be further analyzed asshown in FIG. 11, which enhances the logic shown in FIG. 10, wherewithin each fractional value of parameter k (i.e., pump position) thetype of fluid can be identified using the sensor level cutoff methodshown in FIG. 8. Within the bins or categories, the test for fluid typeis made so that a new attribute can be added, such as color, based onthe fluid type.

The initial logic steps in FIG. 11 could be based on the sensor cutoffas shown in FIG. 8. The categories or bins can be further analyzed withan additional logic step as shown in FIG. 11. For example, as anadditional step, the data could be averaged over incremental timeperiods in each bin to create smoothed binned curves. Further logicsteps can be added depending on the sensor data providing the bestindication of fluid changes in the system over a parameter such as timeor events.

FIGS. 24-29 relate to another apparatus and method for measuring fluidproperties. Fluid sensors tend to provide erratic results when samplingmultiphase fluids. These conditions are encountered when sampling oil inthe presence of water-based mud, water in oil-based mud and gas ineither water-based mud or oil-based mud.

FIGS. 24-26 illustrate a pump 202 with a piston 212 within a chamber216. The pump 202, in an option, is a double-acting pump in which adog-bone style piston moves up and down. When an upper chamber 217 isfilling with fluid, phases of the fluid can separate with the lightergas at the top, then oil and water. The upper chamber 217 of the pump202 is filled with fluid that has segregated and when the piston 202,moves down, as indicated by the arrow, water, oil and gas are expelledsequentially. For instance, as the piston 212 moves down, water isinitially expelled from the lower chamber 219 through the flow linesfollowed by the oil and gas as the piston 212 progresses. The reversehappens when the piston 212 travels upward. A sensor is further includedwith the system, such as a density sensor that provides density sensormeasurements taken at various parameter, such as piston stroke position.

In analyzing the density sensor data relative to the parameter of pistonstroke, the measurements followed the sequence of the piston movementsas shown in FIG. 27, which shows changes in density that correspond tothe segregated fluids and pump position as the fluids leave the pump202. When the piston 212 changes direction, there may be some remainingresidual fluid from the last stroke trapped in between the pump 202 andthe check valves. When the piston 212 finishes a down or upstroke thissmall volume of trapped residual fluid is expelled before the fluids inthe pump chamber are expelled. Since this trapped fluid is from the endof a pump stroke it will contrast in density with the fluid in the pumpchamber at the beginning of the piston stroke. This contrast causes thespikes in the density curve when the pump reverses.

Fluid segregation can also be detected using the density sensor. Thismakes it possible to estimate the relative volumes of the fluid beingpumped during each pump stroke, as illustrated in FIG. 28, where thedensity is divided into bins or groups that represent transitions offluid types. A bin or group, in an option, is defined as a 0.1 gm/ccdensity range. In an option, industry standard colors represent the fourbasic fluid types. Green, represented by 380, represents a typical oildensity range, red, represented by 382, represents gas, blue representedby 384, represents water, and brown, represented by 386, represents mud.Other colors are used to represent the transition from one basic fluidtype to another. By adding up the number of measurements that existwithin each fluid density bin and dividing by the total number ofmeasurements made during a pump stroke, a map of the fluid distributioncan be made. This process can be repeated for successive pump strokesand a time log constructed showing the progression of the fluiddistribution as shown in FIG. 28. Another method of plotting this fluiddistribution log is to update the fluid distribution at regular timeintervals while still summing over a representative volume displaced bythe pump.

Another curve is shown on the time log in FIG. 29, which is the averageof all the density values over the same representative volume. Thisbase-line density curve is a method of averaging that correlates withthe fluid distribution map. The base-line density curve shown in FIG. 29is plotted over the fluid distribution map. As shown, this curveresponds to fluid concentration changes and is therefore an indicator ofsample cleanup. This averaged curve can be used to estimate thecontamination using a trend analysis whereby a regression is used tomatch the density curve to a function representing cleanup and thecontamination is estimated by how close the curve is approaching itsasymptote. Density-based contamination compares favorably to othersensor measurements such as NMR T1, capacitance, and resistivity sensorsusing this trend analysis approach.

When pumping starts, mud filtrate contaminant enters the flowline.During this period of single-phase flow, the density response isconsistent and variations are minor. As pumping continues, and animmiscible fluid arrives in the flowline, the pump starts to segregatethe fluids and the sensors start to show the variation in the fluidproperties. This behavior normally occurs with immiscible fluid mixturesencountered when sampling oil in the presence of water-based mud as wellas water sampling with oil-based mud. While the pump induces fluidsegregation and associated immiscible density response pattern depictedin FIG. 27, a similar behavior is observed by a resistivity sensorpositioned on the inlet side of the pump near the probe. This seeminglyerratic resistivity sensor behavior was previously dismissed as anunreliable sensor measurement. When a processing method describedearlier for the density sensor is applied, there is a surprising trendcorrelation over the duration of the pump-out. Both sensors show similarfluid distribution maps and transitions from contamination to a cleansample, where log examples can be used to demonstrate this.

It should be noted that the methods described herein can be executed initerative, serial, or parallel fashion. Information, includingparameters, commands, operands, and other data, can be sent andreceived, and perhaps stored using a variety of media, tangible andintangible, including one or more carrier waves.

Upon reading and comprehending the content of this disclosure, one ofordinary skill in the art will understand the manner in which a softwareprogram can be launched from a computer-readable medium in acomputer-based system to execute the functions defined in the softwareprogram. One of ordinary skill in the art will further understand thatvarious programming languages may be employed to create one or moresoftware programs designed to implement and perform the methodsdisclosed herein. The programs may be structured in an object-orientatedformat using an object-oriented language such as Java or C++.Alternatively, the programs can be structured in a procedure-orientatedformat using a procedural language, such as assembly, FORTRAN or C. Thesoftware components may communicate using any of a number of mechanismswell known to those skilled in the art, such as application programinterfaces or interprocess communication techniques, including remoteprocedure calls. The teachings of various embodiments are not limited toany particular programming language or environment. Thus, otherembodiments may be realized.

FIG. 12 is a block diagram of an article 400 according to variousembodiments of the invention. The article 400 comprises an article ofmanufacture, such as a computer, a memory system, a magnetic or opticaldisk, some other storage device, and/or any type of electronic device orsystem. For example, the article 400 may include a processor 404 coupledto a computer-readable medium such as a memory 402 (e.g., fixed andremovable storage media, including tangible memory having electrical,optical, or electromagnetic conductors) having associated information406 (e.g., computer program instructions and/or data), which whenexecuted by a computer, causes the computer (e.g., the processor 404) toperform a method including such actions as measuring formation fluid ina borehole and obtaining data, the data having measurement levels thatvary over a parameter, grouping data in one or more categories, eachcategory having data falling within a range, and analyzing the groupeddata as a function of the parameter. In fact, any of the activitiesdescribed with respect to the various methods above may be implementedin this manner.

The following are examples using the various methods, systems, and downhole tools discussed above. FIG. 13 illustrates an example of densitymeasurements plotted over two pump cycles, where the densitymeasurements are plotted as a function of volume. For instance, themeasurement can be taken at every 100 cc of fluid ejected, for a cyclethat ejects 1000 cc overall. In the example shown in FIG. 13, it can beseen there is 10% water (1 g/cc), 60% oil (0.7 g/cc), 10% gas (0.4g/cc), and 20% transitional or heterogeneous fluid. FIG. 14 illustratesanother example of density measurements plotted over two pump cycles. Inthis example, it can be seen there is 40% water (1 g/cc), 30% oil (0.7g/cc), 10% gas (0.4 g/cc), and 20% transitional or heterogeneous fluid.The transitional fluid may occur as the fluid transitions from water tooil, and from oil to gas, where the fluid may form an emulsion or acombination of both fluids. The fluid segregation is dynamic andeventually will be all or mostly oil. However, this method allows thedensity variation to be correlated with a parameter, such as a pumpcycle.

Referring to FIG. 15, the top portion of the graph illustrates anexample of a sample having been drawn into a pump cylinder anddischarged through a density sensor. The density curve varies from 1g/cc to 0.7 g/cc, and shows the sample is 50% water and 50% oil byvolume. The large variation in fluid density indicates the fluid beingpumped is heterogeneous. The lower portion of the graph at 252illustrates how the curve 250 may be referenced to the pump strokepiston position. If segregation occurs in the pump, the densityvariation may be correlated to the pump cycle.

The density or other types of sensor measurements can be sampled atdifferent points referenced to the movement of fluid. The movement offluid may be indicated by the measurement of a parameter, such as, butnot limited to time, pressure, velocity, pump piston position, pumpcontrol, flow rate, valve states or a combination of these. In theexample, pump piston position can be used to group data. For instance,data can be grouped at a pump piston position at 0% (260), 50% (262),60% (264), and 100% (266). Using these groupings, the data can beanalyzed to identify the fluid types as a function of the piston stroke.In this example, the bin 0% (260)=1 g/cc, 50% (262)=1 g/cc, 60%(264)=0.7 g/cc, 0% (266)=0.7 g/cc. The data can be used to identifyinflection points, at the beginning and end of the bins.

As shown in FIG. 15A, between 0 and 50% of the pump stroke, the fluid iswater. As shown in FIG. 2, the valves can be opened and take a down holefluid sample during the water phase of the pump stroke. In addition, theoil portion of the pump stroke could be placed into a separate samplebottle during the 60% to 100% of the pump stroke. The timing andmechanism of the sample collection may be affected by volume of the flowline, compressibility of the fluid, timing of the individual valves,system delays, which can be compensated for during the sample process.

FIG. 15B illustrates the density data displayed using the data collectedfrom the four groups or bins (260, 262, 264, 266 of FIG. 15A) referencedto pump position. Using the bins, the transition from water to oil canbe determined, and that it takes placed between 50% and 60% of thepiston stroke. The total number of bins can be increased to improve theresolution of the measurement.

FIG. 16 illustrates raw data taken before the methods herein are used.It is a raw density plot showing the variation seen for a typical pumpout for mud/filtrate to oil. In the example shown the density variesfrom 1.1 g/cc to 0.8 g/cc. FIG. 17 illustrates an example where theweighted average logic of FIG. 9 is applied to the data of FIG. 16,where the analysis of the data creates the weight average curve of FIG.17.

FIGS. 18, 19A and 19B illustrate an analysis of the raw sensor data ofFIG. 16, using the method of FIG. 10. In FIG. 18, the equal pump strokeperiods create curves showing the fluid distribution related to the pumpposition in the 0, 5%, 10% and 15% position (up and down strokes). Thisrepresents an example of when water would likely be in the grouping orbin. When heterogeneous fluid is present, the binned data is scatteredas shown in FIG. 10. When the fluid is homogeneous, the data becomesstacked.

In FIG. 19A, only selected portions of the pump stroke are shown whichbetter represent the formation oil sample being pumped, which in thiscase are the lighter fluids and uses pump displacement bins of, 45%, 50%and 55% (up and down strokes) In FIG. 19B, the other pump stroke binsare shown which plots the still lighter fluid curves indicating thepresence of gas and uses pump displacement bins of 90 and 95% (up anddown strokes)

FIG. 20 represents an example of binned curves using the logic in FIG.11, applied to the density data shown in FIG. 16. This is a plot showingthe pump position on one scale divided into equal bins from 0 to 1.Within these bins a color represents the fluid being expelled from thepump at that pump position. As pumping continues the plot is filled inwith the shading graphically representing the fluid fractions whilepumping stroke by stroke.

FIG. 32 represents another example of using the logic applied to rawsensor data. The early pump out sensor profile has an initial densitythat varies around 1.04 g/cc indicating the initial presence of wholemud and fines. The density soon decreases to 1.025 g/cc. The initialresistivity response was 0.23 ohm-m while whole mud and fines werepresent. The resistivity soon decreases to 0.18 ohm-m, indicatingfiltrate flowing from the formation. The density data indicates noformation water was present. The water phase density decreases from1.025 g/cc to 1.02 g/cc indicating that the water present during thistime was filtrate rather than formation water. Distilled in situ waterdensity is present and establishes both hydrocarbon indication in manycases and the minimum expected density of water. The first indication ofhydrocarbons occurred after 30 liters of fluid was pumped at a flow rateof 25 cc/sec. The measured density fell below the distilled in situwater density value, which was calculated and presented real-time, 0.99g/cc in this case, confirming some hydrocarbon probability. Review ofthe fluid density distribution track indicated the relative volume offluid with hydrocarbon density ranging from 0.9 to 1.0 g/cc was lessthan 5% until plot time 8:59.

Soon thereafter, the relative volume of fluid with density ranges from0.8 to 0.9 g/cc increasing to 10%. Taking into account variable pumpefficiency, the fluid density distribution track represents 1 liter ofpumped volume from left to right and is color coded in bins of 0.1 g/ccand ranging from 0.3 to 1.3 g/cc. A steady cleanup trend from waterbased mud filtrate to oil is indicated in both the fluid density andresistivity volumetric distribution tracks. A steady clean up trend isalso indicated by the change in baseline density from a value above thedistilled water cutoff to approximately 0.82 g/cc. The density minimavalues are nearly constant after the oil volume surpassed 40%. Theseemingly erratic 0.25 g/cc variation is expected in an immiscible fluidsystem. The minima and maxima values represent the densities of the oiland water phases respectively.

The water hold up probability is also determined based on a capacitancemeasurement and the result is displayed in Water track 8. The waterholdup surrounding the capacitance sensor tends to increase when fluidis diverted around the sensor and water settles around the sensor. Thisno-flow condition is necessary for reducing MRILab T1 fit error and itis indicated by the lack of blue flag shading (“MRFA” Track 9).

Two sequential samples were collected after pumping 13 hours. Prior todelivering the samples to the lab, review of the density data led tovolumetric contamination predictions of 12% (sample #2) and 7.6% (sample#2A). The lab reported the first sample #2 was 10% contaminated byweight and sample #2A was 5% contaminated.

FIG. 21 illustrates another example of a bin method where the value ofthe density measurements can be correlated to the position of the pumppiston. The bins of the pump piston position are the 0%, 20%, 50%, 80%,and 100%, where each of these are plotted in FIG. 21. The individual binor group indicates the percentage of the cylinder where the measurementis made. Using this method, the density of the water, oil and gascondensate may be measured well before the fluid is 100% clean. Bylooking at the transition between water to oil, a volumetric estimationcan be determined considering what percentage the bin indicates aspecific density. In addition, heterogeneity can be determined.

In an option, the sensor will depend on which bins are used formeasurements. The 0% bin which makes the measurement of the first fluidthat leaves the cylinder will have the greatest density fluid, this binwould be best suited to make the water phase measurement and may set the100% contaminated point of the sample fluid. Using the same method, themost reliable gas condensate or early stage oil measurements would bemade by the 100% bin. As can be seen, the 100% bin showed the firstshowing of oil and as the contamination in the cylinder reduced more andmore bins confirmed the existence of oil. The example shown in FIG. 21shows a gas condensate sample as all the bins cleaned up to 100% gascondensate. FIG. 22 shows the water bin 280 never transitioning to alighter fluid. In a contaminated system, there may always be some levelof contamination, depending on the fluid being sampled, and there maynot be any indication of gas. If at any point, all or a large percentageof the bins read the same value, the fluid can then be determined to behomogenous fluid of the same fluid properties.

FIG. 23 is an example of a plotted data using the methods herein,showing a fluid sample transitioning from a density of 1.05 g/cc to 0.83g/cc. The data measurements are binned or grouped into three bins basedon piston stroke. At 290, there is a transition to water. At 292, thebin having the 50% stroke position has oil. At 294, all of the bins aremeasuring oil. When the bin density separates, it can be assumed thefluids are heterogeneous and analysis of the bin volumes may enable amixing or heterogeneity index to be calculated. This measurement canalso be made in a time base where the sample rate is determined by thevelocity and or rate of the fluid.

FIGS. 30 and 31 illustrate a flow diagram showing an example of logicthat can be used to create volume fraction curves from fluid sensorreadings. In both FIGS. 30 and 31, one pump stroke cycle is used fordetermining the volume fractions because the pump tends to induce thevariations in the sensor measurements and fluid phases, as discussedabove. Using a pump stroke cycle is not a limitation of this method,however. Any amount of either volume of time can be used.

In FIG. 30, volume fractions are based on fixed cutoff levels that areequally spaced intervals of the sensor readings (i.e. 0.1, 0.2, 0.3, 0.4. . . 1.0). These intervals or bins can also be assigned colors thatcharacterize the type of fluids expected within the bins. The intervalsor bins may or may not be equally spaced. The spacing depends on thesensor property being measured and the relationship it has to the volumefractions or different fluid types or phases. Based on the number ofcutoffs or bins, n, a number of volume fraction curves are developed bycounting the readings that fall between the cutoffs and the dividingthem by the total number of readings during a pump stroke, m. Thestacked volume fraction curves can be plotted in a single track with thespacing between the curves representing the volume fraction of eachbinned flow component. The spaces between the curves can be shaded torepresent the fluid types.

Weight average curves are developed based on a floating cutoff with thelogic shown in FIG. 31. The sensor data, such as density, is scannedduring a pump stroke to determine the maximum and minimum readingdetected (S_(max), S_(min)). A fraction between the S_(max) and S_(min)is chosen to determine how to create the weighted average over the pumpstroke (usually 50%). Readings above this floating cutoff are summed andaveraged to determine a curve that is weighted to the highermeasurements and another curve weighted to lower measurements. Thesimple average of the sensor reading is also determined over the volumeto time interval chosen.

In FIG. 31, the logic is shown for determining maximum and minimum ofthe sensor measurements over the pumping sequence, such as the entiresequence (S_(maxx), S_(minx)). The S_(maxx) and S_(minx) represent thesensor measurement extremes of the sensor measurement, which can beeither the filtrate or formation fluids. As the S_(av) approaches eitherof these extremes, this is an indication of cleanup. To estimate thecontamination the difference between the sensor average, S_(av), and theextremes is determined and divided by the difference between the sensorextremes. The minimum of the two estimates is the contamination estimateover the volume or time interval C(i) as shown in FIG. 31.

The weighted volume fraction curves can be used to estimate theheterogeneity of the flow, as further discussed above. Thisheterogeneity curve Het(i) is the difference between the weighted volumefraction curves divided by the difference between the sensor extremesover the entire sampling sequence. The heterogeneity curve is a measureof how well the fluids are mixed and which normally converges whencleanup occurs.

Implementing the apparatus, systems, and methods of various embodimentsmay provide the ability to determine fluid types and heterogeneity withgreater accuracy than was previously achieved.

The accompanying drawings that form a part hereof, show by way ofillustration, and not of limitation, specific embodiments in which thesubject matter may be practiced. The embodiments illustrated aredescribed in sufficient detail to enable those skilled in the art topractice the teachings disclosed herein. Other embodiments may beutilized and derived therefrom, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. This Detailed Description, therefore, is not to betaken in a limiting sense, and the scope of various embodiments isdefined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R.§1.72(b), requiring an abstract that will allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the claims. In addition, in the foregoing DetailedDescription, it can be seen that various features are grouped togetherin a single embodiment for the purpose of streamlining the disclosure.This method of disclosure is not to be interpreted as reflecting anintention that the claimed embodiments require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment.

What is claimed is:
 1. A method for sampling formation fluid, the methodcomprising: introducing formation fluid into a pump, the pump having apump piston, an inlet, and an outlet; segregating the fluid within thepump; pumping the fluid out through a flowline by action of the pump;sensing the fluid with a fluid sensor at or downstream of the outlet ofthe pump to obtain sensor data; operating on the sensor data using aprocessor to identify a fluid type correlated to position of the pumppiston; and selectively opening and closing a valve to accept a desiredfluid into at least one sample chamber based on the sensor datacorrelated to the position of the pump piston and the fluid type,wherein the valve is opened and closed during the pumping.
 2. The methodas recited in claim 1, wherein the valve is selectively opened andclosed to accept a first fluid type into a first sample chamber, and toaccept a second fluid type into a second sample chamber.
 3. The methodas recited in claim 1, wherein the sensing includes sensing a density ofthe fluid.
 4. The method as recited in claim 1, wherein the method isperformed until at least one sample chamber is filled.
 5. The method asrecited in claim 1, wherein the method includes obtaining sensor datacorrelated to position of the pump piston in terms of fractions of pumpdisplacement.
 6. The method as recited in claim 1, wherein the pumpassists in segregating the fluid.